Stable-Structure-Guided Spectral Corruption for Diffusion-Based Multivariate Time Series Anomaly Detection

Diffusion-based reconstruction has shown promising results in multivariate time series anomaly detection, using discrepancies between observations and their reconstructions to identify anomalies. However, isotropic corruption may affect the input information available to the denoiser, raising the question of how to design anisotropic corruption based on structural characteristics. Existing frequency-aware diffusion methods typically allocate corruption under prior assumptions that equate absolute frequency location or spectral energy with information value; these assumptions may overlook normal high-frequency transients and low-energy frequency components that encode reproducible, stable structures. To address this issue, we propose a stable-structure-guided diffusion framework. The framework constructs adaptive soft frequency bands from local window features and combines local spectral prototype support, cross-window reproducibility, and cross-variable relational consistency provided by a stable reference bank to form band-level structural evidence. This evidence guides the allocation of corruption strengths to preserve components supported by stable patterns during denoising. Across five multivariate benchmark datasets, the framework improves mean F1 by 33.10% over the state-of-the-art diffusion model and by 24.86% over the state-of-the-art model among all evaluated baselines, which comprise 41 representative anomaly detection methods.